Books

2. The Role of Neural Networks in Cognitive Radar
S. Z. Gurbuz, S. Bruggenwirth, T. Reininger, A. C. Gurbuz and G. E. Smith
in Next Generation Cognitive Radar Systems, K. V. Mishra, B. Shankar M. R. and M. Rangaswamy (Eds.), IET, December 2023. ISBN 978-1-83953-474-4

This chapter examines how neural networks are reshaping cognitive radar. It reviews learning-based approaches within the radar perception–action cycle — from adaptive waveform design and resource management to target classification — and contrasts data-driven methods with classical model-based processing. The discussion also addresses the practical obstacles to deploying deep learning in operational radar: limited training data, physics consistency, interpretability, and real-time computation.

@incollection{gurbuz2023neural,
  author    = {S. Z. Gurbuz and S. Bruggenwirth and T. Reininger and A. C. Gurbuz and G. E. Smith},
  title     = {The Role of Neural Networks in Cognitive Radar},
  booktitle = {Next Generation Cognitive Radar Systems},
  editor    = {K. V. Mishra and B. Shankar M. R. and M. Rangaswamy},
  publisher = {IET},
  year      = {2023},
  isbn      = {978-1-83953-474-4},
  doi       = {10.1049/sbra552e_ch12}
}

1. Basic Principles of Machine Learning
A. C. Gurbuz and F. Ahmad
in Deep Neural Network Design for Radar Applications, S. Z. Gurbuz (Ed.), IET, December 2020. ISBN 978-1-78561-852-9

Written as the foundational chapter of the book, this contribution introduces the mathematical principles of machine learning for a radar and remote sensing audience. It covers supervised and unsupervised learning, feature representation, optimization and regularization, model selection and generalization, and the building blocks of neural networks — providing the background needed for the deep learning radar applications developed in later chapters.

@incollection{gurbuz2020basic,
  author    = {A. C. Gurbuz and F. Ahmad},
  title     = {Basic Principles of Machine Learning},
  booktitle = {Deep Neural Network Design for Radar Applications},
  editor    = {S. Z. Gurbuz},
  publisher = {IET},
  year      = {2020},
  isbn      = {978-1-78561-852-9},
  doi       = {10.1049/sbra529e_ch2}
}